Mall-Customer-Segmentation

Project 2 : Mall Customer Segmentation

dataset-cover

DATASET USED :

Kaggle Mall Customer Segmentation Data (for learning purpose)

Screenshot (445)

Tech Stack used:

  1. Numpy
  2. Pandas
  3. Sklearn
  4. Matplotlib

    Code

Importing libraries

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

Data Collection and Analysis

dataset_customers=pd.read_csv('/content/Mall_Customers.csv')
print(dataset_customers.head())
X=dataset_customers.iloc[:,[3,4]].values #only need last two columns

Finding Apt Number of Clusters

min_dis_sum=[]
for i in range(1,11):
    model= KMeans(n_clusters=i,init='k-means++',random_state=23)
    model.fit(X)
    min_dis_sum.append(model.inertia_)

ELBOW POINT GRAPH

plt.plot(range(1,11),min_dis_sum)
plt.title('The Elbow point graph')
plt.xlabel('Number of clusters')
plt.ylabel('Within Clusters Sum of squares ')
plt.show()

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Appropriate or optimum number of clusters = 5

Plot Within Clusters Sum of squares to number of clusters

model=KMeans(n_clusters=5,init='k-means++',random_state=0)
Y=model.fit_predict(X) #return a label for each data point on their cluster

Visualizing the 5 clusters and their centroids

plt.figure(figsize=(8,8))
plt.scatter(X[Y==0,0],X[Y==0,1],s=50,c='green',label='cluster 1')
plt.scatter(X[Y==1,0],X[Y==1,1],s=50,c='yellow',label='cluster 2')
plt.scatter(X[Y==2,0],X[Y==2,1],s=50,c='cyan',label='cluster 3')
plt.scatter(X[Y==3,0],X[Y==3,1],s=50,c='violet',label='cluster 4')
plt.scatter(X[Y==4,0],X[Y==4,1],s=50,c='blue',label='cluster 5')
plt.scatter(model.cluster_centers_[:,0],model.cluster_centers_[:,1],s=100,c='red',label='Centroids')
plt.title('Customer Groups')
plt.xlabel('Annual Income')
plt.ylabel('Spending score')
plt.show()

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